Samsara’s Focus on Solutions Integration Highlights the Growing Importance of AI Implementation in Fleet and Asset Management for Enterprises

Industrial Internet of Things (IIoT) company Samsara held its annual Beyond user conference in Chicago from June 26-28. Samsara also conducted an analyst summit on the first day of the user conference. The summit featured Questions and Answers (Q&A) with the leadership team and roundtables with Small and Medium Enterprises (SMEs) (covering telematics, safety, and hardware), in addition to a customer site visit at Farmer’s Fridge and a fireside chat with GardaWorld Security Services regarding fleet operations. The keynote of the user conference was headlined by co-founder and Chief Executive Officer (CEO) Sanjit Biswas, who announced a number of new offerings, including:

• Enterprise-Grade Asset Tag: Samsara launched the industry’s first enterprise-grade Asset Tag designed to meet customer demand for tracking and managing small, high-value assets. This new device leverages the Samsara network to offer increased visibility into the location of mission-critical equipment and tools. As a result, organizations can minimize downtime spent searching for lost or stolen items, reduce associated costs, and simplify inventory management. Loss prevention, stolen asset recovery, downtime reduction, and better inventory management are some of the prominent use cases Samsara expects users to gain.
• Connected Training and Workflows: Samsara’s Connected Workflows are an enhancement over last year’s Connected Forms. This tool takes digitization a step further and makes it actionable. It goes beyond digitization to orchestrating multi-step workflows. Users can automatically assign forms, manage approvals, and create tasks based on contextual insights like entering a geofence or detecting a vehicle crash. In addition, this solution also aims to incorporate different functions across the organization like Human Resources (HR), finance, procurement, etc.
• Enhanced Video Safety Offerings: Leveraging Machine Learning (ML) models to use dash cam videos to help detect and alert the driver of risks to help them be more proactive in avoiding incidents has been an increasing trend. In light of this, Samsara also introduced Artificial Intelligence (AI)-based innovations on the video safety end. The first two are lane departure and forward collision. The third one is a tricky capability that’s a lot harder to detect—drowsiness detection.

Drowsiness detection works by using an inward-facing dash cam. It works in the same way that AI technology can detect a driver using a mobile phone or not having their seatbelt on. For drowsiness detection, it’s a matter of consolidating more data points. Samsara says its drowsiness detection capability looks for closed eyes, yawning, facial contortion, rubbing eyes, and blank stares.

Drowsiness detection innovations fall in line with what fleets are struggling with. Historically, driver fatigue is the root cause of nearly half of all trucking accidents in the United States in which the driver is at fault. Each accident results in substantial losses for fleets. In addition to vehicle damage, companies face costs from cargo loss, unscheduled downtime, Service-Level Agreement (SLA) breaches (i.e., delivering cargo after an agreed-upon deadline), and insurance claims. In the United States, the National Highway Traffic Safety Administration (NHTSA) found that 91% of car-truck accidents were the fault of the driver of the passenger car. Video telematics and its advancements will be key, so Samsara making advanced video telematics a highlight of its offerings, especially driver behavior monitoring, falls in line with how the industry is shaping up.

When it comes to AI technologies, the more points of data available, the better they function. Samsara has a seemingly effective approach and it has named the framework a “data flywheel.” Basically, data powers insights, insights fuel action, and action leads to more actionable and high-quality data. Samsara can undoubtedly benefit from the fact that its consumer fleets drove over 60 billion miles and made 75 billion Application Programming Interface (API) calls. Fleets being ingrained in a vendor’s ecosystem play a crucial role as well when it comes to utilizing more data points. Farmer’s Fridge leveraging Samsara’s telematics, video monitoring, and cold chain monitoring solutions for streamlining its fleet operations was a good example.

Leveraging more data is crucial for advancing AI-based use cases in asset and fleet management for road-based fleets. Samsara’s focus on this aspect is a testament to this. Future use cases like Vehicle-to-Everything (V2X) integrate Global Positioning System (GPS) data with traffic and weather information, optimize route planning, enhancing fuel efficiency, and improve timeliness. Some large manufacturers and Third-Party Logistics (3PL) players are conducting pilot AI projects to analyze vast amounts of data from their fleets, optimizing routes and reducing fuel consumption. This has the potential to save millions annually. Using AI to achieve quick wins will be crucial to widespread AI adoption.